



'''simple docstring'''

import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  logging.get_logger(__name__)
logging.set_verbosity_info()






def 				a  (     UpperCamelCase_    :    str      ,					UpperCamelCase_    :    str			)       ->	str:
		if "xprophetnet" in prophetnet_checkpoint_path:
				snake_case__	     =XLMProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase_			)
				snake_case__     ,	snake_case__	     =XLMProphetNetForConditionalGeneration.from_pretrained(
				    UpperCamelCase_      ,					output_loading_info=UpperCamelCase_			)
		else:
				snake_case__	     =ProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase_			)
				snake_case__     ,	snake_case__	     =ProphetNetForConditionalGeneration.from_pretrained(
				    UpperCamelCase_      ,					output_loading_info=UpperCamelCase_			)

		snake_case__	     =['key_proj', 'value_proj', 'query_proj']

		snake_case__	     ={
		    'self_attn': 'ngram_self_attn',
		    'cross_attn': 'encoder_attn',
		    'cross_attn_layer_norm': 'encoder_attn_layer_norm',
		    'feed_forward_layer_norm': 'final_layer_norm',
		    'feed_forward': '',
		    'intermediate': 'fc1',
		    'output': 'fc2',
		    'key_proj': 'k_proj',
		    'query_proj': 'q_proj',
		    'value_proj': 'v_proj',
		    'word_embeddings': 'embed_tokens',
		    'embeddings_layer_norm': 'emb_layer_norm',
		    'relative_pos_embeddings': 'relative_linear',
		    'ngram_embeddings': 'ngram_input_embed',
		    'position_embeddings': 'embed_positions',
		}

		for key in loading_info["missing_keys"]:
				snake_case__	     =key.split('.'			)

				if attributes[0] == "lm_head":
						snake_case__	     =prophet
						snake_case__	     =prophet_old
				else:
						snake_case__	     =prophet.prophetnet
						snake_case__	     =prophet_old.model

				snake_case__	     =False
				for attribute in attributes:
						if attribute in mapping:
								snake_case__	     =mapping[attribute]
								if not hasattr(UpperCamelCase_      ,					UpperCamelCase_			) and len(UpperCamelCase_			) > 0:
										snake_case__	     =attribute
						elif hasattr(UpperCamelCase_      ,					UpperCamelCase_			):
								snake_case__	     =attribute

						if attribute == "weight":
								assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
								snake_case__	     =old_model.weight
								logger.info(f"""{attribute} is initialized."""			)
								snake_case__	     =True
								break
						elif attribute == "bias":
								assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
								snake_case__	     =old_model.bias
								logger.info(f"""{attribute} is initialized"""			)
								snake_case__	     =True
								break
						elif attribute in special_keys and hasattr(UpperCamelCase_      ,					'in_proj_weight'			):
								snake_case__	     =old_model.in_proj_weight.shape[0] // 3
								snake_case__	     =getattr(UpperCamelCase_      ,					UpperCamelCase_			)
								param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
								param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
								if attribute == "query_proj":
										snake_case__	     =nn.Parameter(old_model.in_proj_weight[:embed_dim, :]			)
										snake_case__	     =nn.Parameter(old_model.in_proj_bias[:embed_dim]			)

								elif attribute == "key_proj":
										snake_case__	     =nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]			)
										snake_case__	     =nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]			)
								elif attribute == "value_proj":
										snake_case__	     =nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]			)
										snake_case__	     =nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]			)
								snake_case__	     =True
								break
						elif attribute == "position_embeddings":
								assert (
								    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
								), "Hidden size has to match"
								assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
								snake_case__	     =nn.Parameter(old_model.embed_positions.weight[:512, :]			)
								snake_case__	     =True
								break

						if attribute.isdigit():
								snake_case__	     =model[int(UpperCamelCase_			)]
								snake_case__	     =old_model[int(UpperCamelCase_			)]
						else:
								snake_case__	     =getattr(UpperCamelCase_      ,					UpperCamelCase_			)

								if old_attribute == "":
										snake_case__	     =old_model
								else:
										if not hasattr(UpperCamelCase_      ,					UpperCamelCase_			):
												raise ValueError(f"""{old_model} does not have {old_attribute}"""			)
										snake_case__	     =getattr(UpperCamelCase_      ,					UpperCamelCase_			)

				if not is_key_init:
						raise ValueError(f"""{key} was not correctly initialized!"""			)

		print(f"""Saving model to {pytorch_dump_folder_path}"""			)
		prophet.save_pretrained(UpperCamelCase_			)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE__     :						int	=  argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--prophetnet_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			SCREAMING_SNAKE_CASE__     :						str	=  parser.parse_args()
			convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)









'''simple docstring'''

import shutil
import tempfile
import unittest
from unittest.mock import patch

from transformers import (
    DefaultFlowCallback,
    IntervalStrategy,
    PrinterCallback,
    ProgressCallback,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    is_torch_available,
)
from transformers.testing_utils import require_torch


if is_torch_available():
			from transformers.trainer import DEFAULT_CALLBACKS

			from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel

class 							a__( snake_case__     ):






			def __init__(     self						)							->							Dict:
					snake_case__	     =[]






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   **_UpperCAmelCase						)							->							List[str]:
					self.events.append('on_init_end'						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   **_UpperCAmelCase						)							->							Dict:
					self.events.append('on_train_begin'						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   **_UpperCAmelCase						)							->							Optional[Any]:
					self.events.append('on_train_end'						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   **_UpperCAmelCase						)							->							int:
					self.events.append('on_epoch_begin'						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   **_UpperCAmelCase						)							->							List[str]:
					self.events.append('on_epoch_end'						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   **_UpperCAmelCase						)							->							Optional[int]:
					self.events.append('on_step_begin'						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   **_UpperCAmelCase						)							->							str:
					self.events.append('on_step_end'						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   **_UpperCAmelCase						)							->							List[Any]:
					self.events.append('on_evaluate'						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   **_UpperCAmelCase						)							->							Dict:
					self.events.append('on_predict'						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   **_UpperCAmelCase						)							->							Any:
					self.events.append('on_save'						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   **_UpperCAmelCase						)							->							Tuple:
					self.events.append('on_log'						)






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase					,   **_UpperCAmelCase						)							->							List[Any]:
					self.events.append('on_prediction_step'						)





@require_torch
class 							a__( unittest.TestCase     ):






			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =tempfile.mkdtemp()






			def 						_lowercase  (     self						)							->							List[str]:
					shutil.rmtree(self.output_dir						)






			def 						_lowercase  (     self					,   _UpperCAmelCase=0					,   _UpperCAmelCase=0					,   _UpperCAmelCase=64					,   _UpperCAmelCase=64					,   _UpperCAmelCase=None					,   _UpperCAmelCase=False					,   **_UpperCAmelCase						)							->							int:
					# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
					# its set to False since the tests later on depend on its value.
					snake_case__	     =RegressionDataset(length=_UpperCAmelCase						)
					snake_case__	     =RegressionDataset(length=_UpperCAmelCase						)
					snake_case__	     =RegressionModelConfig(a=_UpperCAmelCase					,   b=_UpperCAmelCase						)
					snake_case__	     =RegressionPreTrainedModel(_UpperCAmelCase						)

					snake_case__	     =TrainingArguments(self.output_dir					,   disable_tqdm=_UpperCAmelCase					,   report_to=[]					,   **_UpperCAmelCase						)
					return Trainer(
					    _UpperCAmelCase					,   _UpperCAmelCase					,   train_dataset=_UpperCAmelCase					,   eval_dataset=_UpperCAmelCase					,   callbacks=_UpperCAmelCase					,   )






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase						)							->							Optional[Any]:
					self.assertEqual(len(_UpperCAmelCase						)					,   len(_UpperCAmelCase						)						)

					# Order doesn't matter
					snake_case__	     =sorted(_UpperCAmelCase					,   key=lambda _UpperCAmelCase						: cb.__name__ if isinstance(_UpperCAmelCase					,   _UpperCAmelCase						) else cb.__class__.__name__						)
					snake_case__	     =sorted(_UpperCAmelCase					,   key=lambda _UpperCAmelCase						: cb.__name__ if isinstance(_UpperCAmelCase					,   _UpperCAmelCase						) else cb.__class__.__name__						)

					for cba, cba in zip(_UpperCAmelCase					,   _UpperCAmelCase						):
							if isinstance(_UpperCAmelCase					,   _UpperCAmelCase						) and isinstance(_UpperCAmelCase					,   _UpperCAmelCase						):
									self.assertEqual(_UpperCAmelCase					,   _UpperCAmelCase						)
							elif isinstance(_UpperCAmelCase					,   _UpperCAmelCase						) and not isinstance(_UpperCAmelCase					,   _UpperCAmelCase						):
									self.assertEqual(_UpperCAmelCase					,   cba.__class__						)
							elif not isinstance(_UpperCAmelCase					,   _UpperCAmelCase						) and isinstance(_UpperCAmelCase					,   _UpperCAmelCase						):
									self.assertEqual(cba.__class__					,   _UpperCAmelCase						)
							else:
									self.assertEqual(_UpperCAmelCase					,   _UpperCAmelCase						)






			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							Any:
					snake_case__	     =['on_init_end', 'on_train_begin']
					snake_case__	     =0
					snake_case__	     =len(trainer.get_eval_dataloader()						)
					snake_case__	     =['on_prediction_step'] * len(trainer.get_eval_dataloader()						) + ['on_log', 'on_evaluate']
					for _ in range(trainer.state.num_train_epochs						):
							expected_events.append('on_epoch_begin'						)
							for _ in range(_UpperCAmelCase						):
									step += 1
									expected_events += ["on_step_begin", "on_step_end"]
									if step % trainer.args.logging_steps == 0:
											expected_events.append('on_log'						)
									if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
											expected_events += evaluation_events.copy()
									if step % trainer.args.save_steps == 0:
											expected_events.append('on_save'						)
							expected_events.append('on_epoch_end'						)
							if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
									expected_events += evaluation_events.copy()
					expected_events += ["on_log", "on_train_end"]
					return expected_events






			def 						_lowercase  (     self						)							->							Optional[int]:
					snake_case__	     =self.get_trainer()
					snake_case__	     =DEFAULT_CALLBACKS.copy() + [ProgressCallback]
					self.check_callbacks_equality(trainer.callback_handler.callbacks					,   _UpperCAmelCase						)

					# Callbacks passed at init are added to the default callbacks
					snake_case__	     =self.get_trainer(callbacks=[MyTestTrainerCallback]						)
					expected_callbacks.append(_UpperCAmelCase						)
					self.check_callbacks_equality(trainer.callback_handler.callbacks					,   _UpperCAmelCase						)

					# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
					snake_case__	     =self.get_trainer(disable_tqdm=_UpperCAmelCase						)
					snake_case__	     =DEFAULT_CALLBACKS.copy() + [PrinterCallback]
					self.check_callbacks_equality(trainer.callback_handler.callbacks					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =DEFAULT_CALLBACKS.copy() + [ProgressCallback]
					snake_case__	     =self.get_trainer()

					# We can add, pop, or remove by class name
					trainer.remove_callback(_UpperCAmelCase						)
					expected_callbacks.remove(_UpperCAmelCase						)
					self.check_callbacks_equality(trainer.callback_handler.callbacks					,   _UpperCAmelCase						)

					snake_case__	     =self.get_trainer()
					snake_case__	     =trainer.pop_callback(_UpperCAmelCase						)
					self.assertEqual(cb.__class__					,   _UpperCAmelCase						)
					self.check_callbacks_equality(trainer.callback_handler.callbacks					,   _UpperCAmelCase						)

					trainer.add_callback(_UpperCAmelCase						)
					expected_callbacks.insert(0					,   _UpperCAmelCase						)
					self.check_callbacks_equality(trainer.callback_handler.callbacks					,   _UpperCAmelCase						)

					# We can also add, pop, or remove by instance
					snake_case__	     =self.get_trainer()
					snake_case__	     =trainer.callback_handler.callbacks[0]
					trainer.remove_callback(_UpperCAmelCase						)
					expected_callbacks.remove(_UpperCAmelCase						)
					self.check_callbacks_equality(trainer.callback_handler.callbacks					,   _UpperCAmelCase						)

					snake_case__	     =self.get_trainer()
					snake_case__	     =trainer.callback_handler.callbacks[0]
					snake_case__	     =trainer.pop_callback(_UpperCAmelCase						)
					self.assertEqual(_UpperCAmelCase					,   _UpperCAmelCase						)
					self.check_callbacks_equality(trainer.callback_handler.callbacks					,   _UpperCAmelCase						)

					trainer.add_callback(_UpperCAmelCase						)
					expected_callbacks.insert(0					,   _UpperCAmelCase						)
					self.check_callbacks_equality(trainer.callback_handler.callbacks					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Dict:
					import warnings

					# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
					warnings.simplefilter(action='ignore'					,   category=_UpperCAmelCase						)

					snake_case__	     =self.get_trainer(callbacks=[MyTestTrainerCallback]						)
					trainer.train()
					snake_case__	     =trainer.callback_handler.callbacks[-2].events
					self.assertEqual(_UpperCAmelCase					,   self.get_expected_events(_UpperCAmelCase						)						)

					# Independent log/save/eval
					snake_case__	     =self.get_trainer(callbacks=[MyTestTrainerCallback]					,   logging_steps=5						)
					trainer.train()
					snake_case__	     =trainer.callback_handler.callbacks[-2].events
					self.assertEqual(_UpperCAmelCase					,   self.get_expected_events(_UpperCAmelCase						)						)

					snake_case__	     =self.get_trainer(callbacks=[MyTestTrainerCallback]					,   save_steps=5						)
					trainer.train()
					snake_case__	     =trainer.callback_handler.callbacks[-2].events
					self.assertEqual(_UpperCAmelCase					,   self.get_expected_events(_UpperCAmelCase						)						)

					snake_case__	     =self.get_trainer(callbacks=[MyTestTrainerCallback]					,   eval_steps=5					,   evaluation_strategy='steps'						)
					trainer.train()
					snake_case__	     =trainer.callback_handler.callbacks[-2].events
					self.assertEqual(_UpperCAmelCase					,   self.get_expected_events(_UpperCAmelCase						)						)

					snake_case__	     =self.get_trainer(callbacks=[MyTestTrainerCallback]					,   evaluation_strategy='epoch'						)
					trainer.train()
					snake_case__	     =trainer.callback_handler.callbacks[-2].events
					self.assertEqual(_UpperCAmelCase					,   self.get_expected_events(_UpperCAmelCase						)						)

					# A bit of everything
					snake_case__	     =self.get_trainer(
					    callbacks=[MyTestTrainerCallback]					,   logging_steps=3					,   save_steps=10					,   eval_steps=5					,   evaluation_strategy='steps'					,   )
					trainer.train()
					snake_case__	     =trainer.callback_handler.callbacks[-2].events
					self.assertEqual(_UpperCAmelCase					,   self.get_expected_events(_UpperCAmelCase						)						)

					# warning should be emitted for duplicated callbacks
					with patch('transformers.trainer_callback.logger.warning'						) as warn_mock:
							snake_case__	     =self.get_trainer(
							    callbacks=[MyTestTrainerCallback, MyTestTrainerCallback]					,   )
							assert str(_UpperCAmelCase						) in warn_mock.call_args[0][0]




